When training our models and exploring various values for the different model hyper-parameters, we experimented with using the Adam optimizer [1]} with a learning rate of \(1e^{-4}\) , a weight decay of \(1e^{-5}\) , and a Binary Cross-Entropy (BCE) loss as the objective function. We extensively fine-tuned our experimental setups based upon different architectural requirements to select the best hyper-parameter values. We also used early stopping for saving the best intermediate checkpoints. Table REF  gives more detail about the hyper-parameters we used for training. On average, it took approximately 2.5 hours to train a multi-modal neural model.
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